Create handler.py

handler.py

@@ -0,0 +1,47 @@
+import torch
+
+from typing import Any, Dict
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+
+class EndpointHandler:
+    def __init__(self, path=""):
+        # load model and tokenizer from path
+        self.tokenizer = AutoTokenizer.from_pretrained(path)
+        self.model = AutoModelForCausalLM.from_pretrained(
+            path, device_map="auto", torch_dtype=torch.float16, trust_remote_code=True
+        )
+        self.device = "cuda" if torch.cuda.is_available() else "cpu"
+
+    def __call__(self, data: Dict[str, Any]) -> Dict[str, str]:
+        # process input
+        inputs = data.pop("inputs", data)
+        
+        parameters = data.pop("parameters", None)
+
+        # preprocess
+        print(print("inputs......", inputs))
+        inputs = self.tokenizer(inputs, return_tensors="pt").to(self.device)
+        print("inputs......", inputs)
+
+
+
+        t=0
+        for j in range(len(inputs['token_type_ids'][0])):
+            if inputs['input_ids'][0][j]==39 and inputs['input_ids'][0][j+1]== 5584:
+                t=0
+            if inputs['input_ids'][0][j]==39 and inputs['input_ids'][0][j+1]== 13359:
+                t=1
+            inputs['token_type_ids'][0][j]=t
+
+
+        # pass inputs with all kwargs in data
+        if parameters is not None:
+            outputs = self.model.generate(**inputs, **parameters)
+        else:
+            outputs = self.model.generate(**inputs)
+
+        # postprocess the prediction
+        prediction = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+        return [{"generated_text": prediction}]